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Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The focus of this paper is on proposing new schemes based on finger vein patterns by employing block uniform local binary pattern and block two-directional two-dimension principal component analysis method in order to effectively reduce data redundancy. First, a block multi-scale uniform local binary pattern (MULBP) features operator based on improved circular neighborhood is employed to extract the local texture features of finger vein images effectively. Then two-directional two-dimension principal component analysis ((2D)2PCA) method is applied to finger vein recognition in this paper, which can effectively reduce the dimension of feature matrix and improve system performance. Furthermore, in order to avoid the disadvantage of (2D)2PCA method which cannot preserve some important local features, this paper adopts (2D)2PCA method based on block to preserve local information of image. The experimental results revealed that our proposed method achieved superior performance for the FV-USM finger vein database with a recognition rate of99.32 % and consistent performance for the Tianjin finger vein database with a recognition rate more than 99 %. Above results show that this new algorithm is effective and feasible in finger vein recognition system. [ABSTRACT FROM AUTHOR]